Accounting for the Influence of Surface Obstacles on Distributed Wind Power Generation with Diagnostic Wind Solver

Rural and agricultural sites can utilize distributed wind turbines as sources of additional energy. These provide clean, local energy for the individual’s needs and create a more resilient power system. Technological improvements in wind energy are ongoing, leading to more efficient systems and lower installation costs. However, identifying the ideal location for these small wind turbines is key to maximizing the power output.

Numerous obstacles exist in these environments, such as trees and buildings, and considering the factors that may influence power production is vital when selecting the site for a wind turbine. The wake and turbulence caused by obstacles limits the energy output of the turbine, while installation costs usually necessitate the turbine be located nearby to a building. Additionally, the smaller turbines utilized in distributed wind resources (10-60 m) are more strongly affected by obstacles.

As distributed wind turbines become more widely available, customers need tools to help identify the best location for installation. As part of the Department of Energy Tools for Assessing Progress project, we assist with decision making by utilizing high resolution wind modeling that simulates the complex flow around obstacles. We consider the climatological winds at a prospective site in addition to the terrain and physical obstacles. The relative likelihood of winds from each prevailing direction are considered, and thus based on the local climatology, we can determine areas that will be more prone to obstacle shading effects.

The Quick Urban and Industrial (QUIC) model is a diagnostic wind solver that is aware of surface and obstacle conditions. While a computational fluid dynamics (CFD) model may produce higher fidelity simulations, the results are too computationally expensive for customer’s use. The QUIC model can run from a laptop in seconds to identify the locations within a site that will be the most efficient for turbine placement. This is vital to ensure that distributed wind networks are economically viable and that obstacle effects and instal